Part 2_ FPGA Implementation of Automatic Dependent Surveillance–Broadcast Using Matlab Simulink

This example shows how to design packet-based airplane tracking application based on Automatic Dependent Surveillance Broadcast (ADS-B) standard, partitioned between FPGA and embedded processor.

Contents Introduction - Automatic Dependent Surveillance–Broadcast Introduction - SoC Blockset Introduction - Packet-Based ADS-B Transceiver ADS-B Transmitter Algorithm ADS-B Receiver Algorithm Matlab Simulink implementaion Simulation & Results Supported Hardware Platforms: Xilinx® Zynq® ZC706 evaluation kit + Analog Devices® FMCOMMS2/3/4 card. ZedBoard™ + Analog Devices FMCOMMS2/3/4 card. Automatic Dependent Surveillance–Broadcast (ADS–B) is a surveillance technology in which an aircraft determines its position via satellite navigation or other sensors and periodically broadcasts it, enabling it to be tracked.

ADS-B, which consists of two different services, "ADS-B Out" and "ADS-B In", could replace radar as the primary surveillance method for controlling aircraft worldwide.

ADS-B provides many benefits to both pilots and air traffic control that improve both the safety and efficiency of flight.

Traffic : When using an ADS-B In system, a pilot is able to view traffic information about surrounding aircraft if those aircraft are equipped with ADS-B out. This information includes altitude, heading, speed, and distance to aircraft.

Weather : Aircraft equipped with universal access transceiver (UAT) ADS-B In technology will be able to receive weather reports, and weather radar through flight information service-broadcast (FIS-B).

Flight information: Flight information service-broadcast (FIS-B) also transmits readable flight information such as temporary flight restrictions (TFRs) and NOTAMs to aircraft equipped with UAT. This example showed how SoC Blockset is used to design packet-based ADS-B standard to meet system requirements.

By simulating the design with memory channel as interface between the FPGA and the Processor, we validated that the system requirements of throughput and drop packets are met at the design time.

We implemented the design on SoC device from the model and verified the results on hardware. Although ADS-B is not a computationally intensive standard, it is useful to demonstrate the design process for packet-based systems intended for implementation on a SoC device.

We can follow the same design procedure for even more computationally intensive requirements for this application or another packet-based application. Click here to download the simulink model https://drive.google.com/file/d/1JHyIIEEn64gE2ljG1UOu9aMgkdE56xra/view?usp=sharing https://drive.google.com/file/d/16ODZH3JI3z7Jfe-jomOSWsKYqoyyICLO/view?usp=sharing Kindly Subscribe My YouTube Channel... Please like, share and comments on My Videos 🙏 Please click the below links to Subscribe/Join & View my Videos https: //www.youtube.com/c/DrMSivakumar Telegram : t.me/Dr_MSivakumar website : drmsivakumar78.blogspot.com

Part 1_ Design & FPGA Implementation of Automatic Dependent Surveillance–Broadcast Using Matlab

This example shows how to design packet-based airplane tracking application based on Automatic Dependent Surveillance Broadcast (ADS-B) standard, partitioned between FPGA and embedded processor.

Contents Introduction - Automatic Dependent Surveillance–Broadcast Introduction - SoC Blockset Introduction - Packet-Based ADS-B Transceiver ADS-B Transmitter Algorithm ADS-B Receiver Algorithm Matlab Simulink implementaion Simulation & Results Supported Hardware Platforms: Xilinx® Zynq® ZC706 evaluation kit + Analog Devices® FMCOMMS2/3/4 card. ZedBoard™ + Analog Devices FMCOMMS2/3/4 card. Automatic Dependent Surveillance–Broadcast (ADS–B) is a surveillance technology in which an aircraft determines its position via satellite navigation or other sensors and periodically broadcasts it, enabling it to be tracked.

ADS-B, which consists of two different services, "ADS-B Out" and "ADS-B In", could replace radar as the primary surveillance method for controlling aircraft worldwide.

ADS-B provides many benefits to both pilots and air traffic control that improve both the safety and efficiency of flight.

Traffic : When using an ADS-B In system, a pilot is able to view traffic information about surrounding aircraft if those aircraft are equipped with ADS-B out. This information includes altitude, heading, speed, and distance to aircraft.

Weather : Aircraft equipped with universal access transceiver (UAT) ADS-B In technology will be able to receive weather reports, and weather radar through flight information service-broadcast (FIS-B).

Flight information: Flight information service-broadcast (FIS-B) also transmits readable flight information such as temporary flight restrictions (TFRs) and NOTAMs to aircraft equipped with UAT. This example showed how SoC Blockset is used to design packet-based ADS-B standard to meet system requirements.

By simulating the design with memory channel as interface between the FPGA and the Processor, we validated that the system requirements of throughput and drop packets are met at the design time.

We implemented the design on SoC device from the model and verified the results on hardware. Although ADS-B is not a computationally intensive standard, it is useful to demonstrate the design process for packet-based systems intended for implementation on a SoC device.

We can follow the same design procedure for even more computationally intensive requirements for this application or another packet-based application. Click here to download the simulink model https://drive.google.com/file/d/1JHyIIEEn64gE2ljG1UOu9aMgkdE56xra/view?usp=sharing https://drive.google.com/file/d/16ODZH3JI3z7Jfe-jomOSWsKYqoyyICLO/view?usp=sharing

FPGA HDL Implementation of Contrast Limited Adaptive Histogram Equalization (CLAHE)U sing Simulink

This example shows how to implement a contrast-limited adaptive histogram equalization (CLAHE) algorithm using Simulink® blocks. This is an image contrast enhancement algorithm that overcomes limitations in standard histogram equalization (HE). %CLAHE Algorithm modelname = 'CLAHEExample'; open_system(modelname,'force'); set_param(modelname,'SampleTimeColors','off'); set_param(modelname,'Open','on'); set_param(modelname,'SimulationCommand','Update'); set(allchild(0),'Visible','off'); %Tile Generation system = 'CLAHEExample/CLAHEHDLAlgorithm/tileGeneration'; open_system(system,'force'); %Histogram Equalization Pipeline system = 'CLAHEExample/CLAHEHDLAlgorithm/histoEqPipeline/'; subsystem = [system 'histPipe1']; open_system(subsystem,'force'); %The remaining total excess value is passed to the Redistribute subsystem as excess value. system = 'CLAHEExample/CLAHEHDLAlgorithm/histoEqPipeline/'; subsystem = [system 'histPipe1/redistribute']; open_system(subsystem,'force'); %The bilinear interpolation equation to compute a pixel value in the output image. system = 'CLAHEExample/CLAHEHDLAlgorithm/bilinearInterpolation'; open_system(system,'force'); Simulink Model : Contrast – Limited Adaptive Histogram Equalization  The input image frame is converted to a pixel stream and pixelcontrol bus using a Frame To Pixels block.  The adjusted pixel values are given to the Pixels To Frame block and converted to a frame using the control signals.  The pixel value read from the imgBuffer subsystem is passed to CLAHEHDLAlgorithm for adjustment.  The pixel stream is passed to the CLAHEHDLAlgorithm subsystem for contrast enhancement and is also stored in the imgBuffer subsystem.  While processing, the CLAHEHDLAlgorithm subsystem generates the address to read image data from the imgBuffer subsystem. The Result subsystem shows the input image and output image once all the pixels in the frame have been received by the Pixels To Frame block. Click here to download the simulink file: https://drive.google.com/file/d/1ktc0B7Ov0P9XMGCs-eTIGjWNvfuOK3ZI/view?usp=sharing

FPGA Implementation of Low-Light Enhancement Algorithm_ To Enhance Low-Light Images/Videos Matlab

This example performs LLE by inverting an input image and then applying a de-haze algorithm on the inverted image.

After inverting the low-light image, the pixels representing non-sky region have low intensities in at least one color channel.

The algorithm consists of six stages. Step 1: Scaling & Inversion The input image   is converted to range [0,1] by dividing by 255 and then inverting pixel-wise. Step 2: Dark Channel Estimation The dark channel is estimated by finding the pixel-wise minimum across all three channels of the inverted image. Step 3: Refinement The airlight image from the previous stage is refined by iterative smoothing. This stage consists of five filter iterations with a 3-by-3 kernel for each stage. Step 4: Non-Linear Correction To reduce over-enhancement, the refined image is corrected using a non-linear correction. Step 5: Restoration Restoration is performed pixel-wise across the three channels of the inverted and corrected image. Step 6: Inversion To obtain the final enhanced image, this stage inverts the output of the restoration stage, and scales to the range [0,255].

The figure shows the input image and the enhanced output images obtained from the LLESimplified subsystem and the LLEHDL subsystem. Click here to get the simulink file: https://drive.google.com/file/d/1nBBRRzjqXsKmImOCqdXWTZ8NXM6hmi3m/view?usp=sharing

Implementation of Haze Removal Algorithm to Enhance Low-Light Images Using Matlab

This example shows how to enhance low-light images using an algorithm suitable for FPGAs.

Haze removal or image dehazing is required in real-world weather conditions to obtain a fast and high-quality hazy free image which is used in various fields like satellite systems and aircraft systems.

Some of the wide important areas in which the haze removal methods used are air- crafts, remote sensing, intelligent transportation systems, underwater image processing, Object detection, outdoor surveillance, railway systems, aerial imagery, computational photography/vision applications, video analysis and recognition, image classification, military and defense surveillance system, etc. Example Code: % Import an RGB image captured in low light. A = imread('lowlight_21.jpg'); % Invert the image. AInv = imcomplement(A); % Apply the dehazing algorithm. BInv = imreducehaze(AInv,'ContrastEnhancement','none'); % Invert the results. B = imcomplement(BInv); %Display the original image and the enhanced images, side-by-side. montage({A,B}); click here to download the Matlab Livescript file: https://drive.google.com/file/d/1S8iBE42W0akwTQp2BksTsSd-NRb5MvV5/view?usp=sharing